In determining quality assurance test figures for equipment, various methods exist for calculating growth rates and return rates. One such method has been proposed by Duane, J. T., Learning Curve Approach to Reliability Monitoring IEEE Transactions on Aerospace, Vol. 2, No. 2, 196, in which the relationship between failure rate and time is a straight line when plotted on log-log paper. This model is relatively easy to use, however, this model assumes that equipment is fixed immediately after a failure occurs, before further test time is accumulated. Such fixes are not normally achieved so quickly in practice.
In order to use the Duane model, data elements including failure history, serial number, failure symptoms, number of items under test, installed date and root cause analysis are gathered and analyzed. Analysis involves calculating cumulative in-service times (CIST) and cumulative percent failures per year and plotting these values to determine a slope and intercept, the slope and intercept being the growth rate and growth constant respectively. Knowledge of the growth rate and growth constant enable a return rate prediction to be made for a given shipping rate of the equipment.
The gathering of data elements can be a tedious operation and the calculation of cumulative in-service times and cumulative percent failures per year can also be tedious. What would be desirable therefore is a way to organize data elements in such a way that the calculation of cumulative in-service times (CIST) and cumulative percent failures per year is simplified for expediency.